In pursuit of evolvable, sustainable, and submissive networks

Luiz DaSilva

Stokes Professor in TelecommunicationsTrinity College,University of DublinIreland

About CTVR….

• CTVR is a national centre for telecommunications research. It is head‐quartered in Trinity College but based in six institutions around Ireland

• Our mission is to carry out industry‐informed research in telecommunications to the highest of standards

• We work both in the optical and wireless domains

Wireless & Optical 

Technologies

OpticalTechnologies

Wireless Technologies

Wireless &  Optical Networks

architecture

architecture

architecturearchite

cture

Other CSETs & SRCs

InternationalCollaborations

Visiting positions

FP7, Future InternetProgrammes & Others Industry

evolvable:Elegant paths to future.  Adaptive and flexible where it should be. Understanding of points of in‐flexibility.

submissive:Not biased towards any one model of ownership. No un‐necessary rule‐setting or resource labelling. 

sustainable:Persistent awareness  of resource constraints e.g. man‐power, energy, bandwidth, processing power, space, storage etc.

interests

transient ownership of resources

ability to learn

decision making under partial information

fundamental principles that will allow the wireless network of the future to evolve into new architectures characterized by increasing autonomy and ubiquity of wireless services

Sustainable, evolvable, submissive: cognitive networks

Cognitive networks – perceive their environment, then decide, learn and act from the results, with network‐wide goals

Cognitiveelement

element goal

cognitive element

element goal

end-to-end goalend-to-end goalend-to-end goal

cognitive element

element goal

cognitiveelement

element goal(s)cognitiveelement

element goal

cognitiveelement

element goal

CSL

RequirementsLayer

CognitiveProcess

SoftwareAdaptableNetworkconfigurable

elementconfigurable

element

configurable element

networkstatussensor

networkstatussensor

cognitive element

element goal(s)

elementstatus

transfer

SAN API

R. W. Thomas, D. H. Friend, L. A. DaSilva, and A. B. MacKenzie, “Cognitive Networks: Adaptation and Learning to Achieve End‐to‐end Performance Objectives,” IEEE Communications Magazine, Dec. 2006

Organization of wireless access networks, including spectrum management, and distributed adaptations by cognitive radios to meet end‐to‐end objectives

Interaction between wireless access and wired networks, including assignment of users to access points, energy efficiency, flexibility to user mobility

Tools

From cognitive radios to cognitive networks

Distributed coordination for heterogeneous networks(collaboration with UCC)

• Spectrum etiquette for distributed channel selection

• Effects of imperfect network information on radio adaptations

• Small / large cell coexistence and resource management

• Analytical (game theory) + experimental

• Sensing and sharing in wireless access and multi‐hop networks

• Heterogeneous handover optimisation• Transport protocols to handle mismatch at wireless/optical boundary

• Analytical (optimisation) + experimental

From Cognitive Radios to Cognitive Networks

Resource management for cognitive radios acting in a network• local adaptations (power, channel, …)• local goals (max SINR, min BER, …)• perfect information (can observe channel, neighbors’ actions)

Current practice

Local adaptations to meet end‐to‐end or network‐wide goals (throughput, network connectivity, …)

Information available to adaptive radios is limited, due to cost of propagating network state information or inaccuracyin observing one’s local environment

Extensions

Coordination for heterogeneous and multi-hop networks

• Distributed spectrum sharing for multi‐hop topologies and HetNets (relays, coexistence between small and large cells)

• Adaptations: channel selection, transmit power

• Goals: network‐wide spectrum efficiency, fairness, network connectivity, coverage

• Cooperative game theory, coalition formation

Types of coalition in equilibrium as a function of link range

Z. Khan, S. Glisic, L. A. DaSilva, and J. Lehtomaki, “Modeling the Dynamics of Coalition Formation Games for Cooperative Spectrum Sharing in an Interference Channel,” IEEE Trans. on Computational Intelligence and AI in Games, 2010

J. E. Suris, L. A. DaSilva, Z. Han, A. B. MacKenzie, and R. S. Komali, “Asymptotic Optimality for Distributed Spectrum Sharing Using Bargaining Solutions,” IEEE Trans. on Wireless Communications, Oct. 2009

Imperfect monitoring

• Impact of incomplete or erroneous information about channel or network conditions or neighbor activity on effectiveness of radio adaptations

• Framework is games of imperfect public/private monitoring

Price of ignorance in topology control adaptations upon departure of one radio

R. S. Komali, R. W. Thomas, L. A. DaSilva, and A. B. MacKenzie, “The Price of Ignorance: Distributed Topology Control in Cognitive Networks,” IEEE Trans. on Wireless Communications, April 2010

Utility ftn:  imperfect public monitoring

π is the expected utility

p is the public signal

Need to model the distribution of p, parameterized by the action vector a

The potential for learning

• Learning is a part of the cognitive cycle: but when it is beneficial to apply machine learning ?  

• Relationship between effectiveness of learning and the amount of pattern that can be observed: Kolmogorov complexity

• Application to frequency‐agile radios

• Evaluation using spectrum measurements at CTVR and RWTH Aachen

Probability of successfully selecting a channel, as a function of complexity in channel use patterns and primary user activity: ISM (top); GSM900 (bottom)

I. Macaluso, D. Finn, B. Ozgul, and L. DaSilva, “Deciding When to Learn: Kolmogorov Complexity and Dynamic Channel Selection,” under review, 2011.

Bringing DSA to LTE/LTE+

J. Deaton, R. Irwin, and L. DaSilva, “The Effects of a Dynamic Spectrum Access Overlay in LTE+ Networks,” IEEE DySPAN 2011.

HSS

PDG(SGW+PGW)

Internet

MME

UE

E-UTRANeNB

eNB

eNB

eNB

eNB

eNB

eNB

GDB

cUE

SAS

cRAN

S1-U

S1-MME

S1-U

S1-MMES11

Diameter (RFC 3588)

eNB: Evolved Node BE-UTRAN: Evolved Universal Terrestrial Radio AccessHSS: Home Subscriber ServerMME: Mobility Management EntityPDG: Packet Data GatewayPGW: Packet GatewaySGW: Signaling Gateway

LTE Network Elements

cBS: cognitive Base StationcRAN: cognitive Radio Access NetworkcUE: cognitive User EquipmentGDB: Geolocation Data BaseSAS: Spectrum Accountability Server

SA Network Elements

cBS

cBScBS

cBS

cBS

cBS

cBS

FRIDAY, 1:30 PM

Special Issue

Cognitive Radio for LTE Advanced and Beyond

• Taking a broad view of cognitive radio  that includes, but is not limited to, DSA

• Letter from the editors / white paper included with CFP

• Deadline: 30 June 20011

FP7 – CREW

Cognitive Radio Experimentation World(2010‐2015)

MODE 1

CREW PORTAL

TCD

TUD

IBBT/IMEC

TUB

CREW PORTAL

MODE 2

RELOCATENODES &

CREATE NEWCOMBINATIONS

CREW PORTAL

MODE 3

STEP 1

STEP 2

REPLAY ONEBEHAVIOURIN OTHER TESTBED

CREW federation modes

CREW open call

• 20% of the budget for the CREW project is set aside to fund new partners who wish to perform experiments using the federated testbeds

http://www.crew-project.eu/

FP7 – COGEU

COGnitive radio systems for efficient sharing of TV white spaces in EUropean context(2010‐2012)

COG-EU objectives

• To design, implement and demonstrate enabling technologies based on cognitive radio to support mobile\portable applications over TVWS with protection of incumbent systems (DVB, PMSE‐wireless microphones)

• To investigate innovative business models and spectrum policies for TVWS exploitation based on secondary market regimes, to increase spectrum utilization enabling innovative wireless services

• COGEU aims to inform EU policy in relation to the enabling of efficient spectrum sharing and usage over TVWS at the European level. 

Architecture for using TVWS instantiates different business models

On the interwebs

About CTVR… www.ctvr.ie

On email… dasilval@tcd.ie

About Luiz… luizdasilva.wordpress.com